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359 results for “Regional modelling”
Mainshock+aftershock M4.95+ seismicity forecasts derived from the Regional Earthquake Likelihood Models (RELM) and the multiplicative hybrid earthquake models developed by Rhoades et al. (2014)
<p>Contains six mainshock+aftershock seismicity forecasts developed by the Working Group of the Regional Earthquake Likelihood Models (RELM) experiment, sixteen multiplicative hybrid forecasts created by Rhoades et al. (2014), and the 2011-2020 M4.95+ ANSS earthquake catalog for California. Six additional forecast files are included to properly conduct the comparative tests implemented in the Collaboratory for the Study of Earthquake Predictability (CSEP) testing centre.</p> <p>Forecasts are stored in tab separated value files with the following fields (the first row of data is shown as an example):</p> <pre>LON_0 LON_1 LAT_0 LAT_1 DEPTH_0 DEPTH_1 MAG_0 MAG_1 RATE FLAG -125.4 -125.3 40.1 40.2 0.0 30.0 4.95 5.05 5.8499099999999998e-04 1 </pre> <p>Forecast are described in detail by the following publications:</p> <p>Bird, P., and Z. Liu (2007). Seismic Hazard Inferred from Tectonics: California. Seismological Research Letters, 78(1):37-48.</p> <p>Ebel, J. E., D. W. Chambers, A. L. Kafka, and J. A. Baglivo (2007). Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California. Seismological Research Letters, 78(1): 57-65.</p> <p>Helmstetter, A., Y. Y. Kagan, and D. D. Jackson (2007). High-resolution Time-independent Grid-based Forecast for M >= 5 Earthquakes in California. Seismological Research Letters, 78(1): 78-86.</p> <p>Holliday, J., Chen, C., Tiampo, K., Rundle, J., Turcotte, D., and Donnellan, A. (2007). A RELM earthquake forecast based on pattern informatics. Seismological Research Letters, 78(1):87–93.</p> <p>Kagan, Y. Y., D. D. Jackson, and Y. Rong (2007). A Testable Five-Year Forecast of Moderate and Large Earthquakes in Southern California Based on Smoothed Seismicity. Seismological Research Letters, 78(1): 94-98.</p> <p>Rhoades, D.A., Gerstenberger, M.C., Christophersen, A., Zechar, J.D., Schorlemmer, D., Werner, M.J. and Jordan, T.H., 2014. Regional earthquake likelihood models II: Information gains of multiplicative hybrids. Bulletin of the Seismological Society of America, 104(6):3072-3083.</p> <p>Shen, Z.-K., D. D. Jackson, and Y. Y. Kagan (2007). Implications of Geodetic Strain Rate for Future Earthquakes, with a Five-Year Forecast of M5 Earthquakes in Southern California. Seismological Research Letters, 78(1):116-120.</p> <p>Ward, S. (2007). Methods for evaluating earthquake potential and likelihood in and around California. Seismological Research Letters, 78(1):121–133.</p> <p>Wiemer, S. and Schorlemmer, D. (2007). ALM: An asperity-based likelihood model for California. Seismological Research Letters, 78(1):134–140.</p>
Model results and configuration files for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>This repository includes files as described below:</p> <p><strong>1. namelist_CBMZ09_example.input, namelist_MOZART202_example.input:</strong></p> <p>Two WRF-chem namelist files for CBMZ and MOZART simulation.</p> <p>They are modified according to the namelist from <a href="https://github.com/wrfchem-leeds/WRFotron">https://github.com/wrfchem-leeds/WRFotron</a>.</p> <p><strong>2. wps_namelist_example.wps:</strong></p> <p>namelist for WRF Preprocessing System (WPS)</p> <p><strong>3. temporal_hourly_scale_factor_emission.csv:</strong></p> <p>Hourly scale factors for emissions.</p> <p>Hourly allocation is applied to all emission data (i.e., emissions for 2017, 2030 and perturbated emissions of NOx, VOCs).</p> <p><strong>4. vertical_emission_ratio.csv</strong></p> <p>Vertical shares (ratios) of emissions.</p> <p>Emissions from sectors of power and industry are vertically allocated based on this file. Vertical allocation is conducted for all emission data.</p> <p>These shares are suggested by MICS-ASIA III intercomparison framework.</p> <p><strong>5. 01_2030_2017_simulations.zip: </strong></p> <p>Simulated MDA8 ozone under future (2030) and 2017 emission scenarios by the two chemical mechanisms (i.e., CBMZ, MOZART).</p> <p><strong>6. 02_perturbations_of_NOxVOCs.zip:</strong></p> <p>Simulated MDA8 ozone given perturbations of NOx and VOCs emissions by the two chemical mechanisms.</p> <p><strong>7. 03_hourly_diff_O3_NOx_OH_HNO3.zip: </strong></p> <p>Differences of hourly simulated concentrations of O3, NOx, OH and HNO3 during July in the Base-2017 scenario between CBMZ and MOZART (CBMZ - MOZART).</p>
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset
<p>Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows: </p> <p> </p> <p>The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R<sup>2</sup>) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.</p>
agroBRIDGES EIP-AGRI Practice Abstracts - Regional insights and business models for SFSC development
<p>The present dataset completed the collection of the 25 regional practice abstracts developed in the framework of the agroBRIDGES project, using the EIP-AGRI common format. The aim is to share project outputs so other actors of the agri-food sector can leverage on them and to see the main added value and benefits met by the end-users if the generated knowledge is implemented. The Practice Abstracts are also published on the EIP-AGRI website and available at this <strong><a href="https://ec.europa.eu/eip/agriculture/en/find-connect/projects/building-bridges-between-consumers-and-producers">link</a>. </strong></p> <p>The current (second) version of the dataset builds upon the previous set of Practice Abstracts generated in each of the 12 European focal countries of the project to share the project's knowledge and insights gained from co-creation, data collection, communication, toolbox validation and event organisation activities.</p> <p>These activities aim at promoting the development of Short Food Supply Chains, while motivating the local network of agri-food actors in the process (multi-actor approach). At least one Practice Abstract has been generated per Beacon Region (i.e. focal region of the agroBRIDGES project). The Practice Abstracts aim to support proactitioners in replicating our approaches and methodologies.</p> <p>2 Practice Abstracts have been elaborated per country (23 in total):</p> <ul> <li>Ireland</li> <li>Denmark</li> <li>Italy (Lazio & Piemonte regions)</li> <li>Greece (Central Macedonia region)</li> <li>Spain (Andalusia region)</li> <li>France (Pays de Loire, Auvergne and Bretagne regions)</li> <li>Poland</li> <li>Latvia</li> <li>Lithuania</li> <li>Turkey</li> <li>Finland</li> <li>Netherlands</li> </ul> <p>Additionally, 1 Practice Abstract was developed to present 5 business model categories of SFSC analysed in the context of agroBRIDGES. </p>
GR4J-REG: regional gridded runoff dataset for northwest Russia based on GR4J model and nearest-neighbor regionalization technique
<p>GR4J-REG: regional gridded runoff dataset for northwest Russia.</p> <p>Model: GR4J.</p> <p>Regionalization technique: Nearest-neighbor.</p> <p>Geographical domain: 25–57°E; 55–70°N.</p> <p>Spatial resolution: 0.5°x0.5°.</p> <p>Temporal resolution: daily.</p> <p>Period: 1979-2016.</p>
The Coastal Streamflow Flux in the Regional Arctic System Model
<p>The Arctic coastal streamflow flux is an important driver of dynamics in the coupled ice-ocean system. We have developed a new streamflow routing model (RVIC), coupled within the Regional Arctic System Model (RASM), to simulate the coastal freshwater flux. RASM is a high-resolution regional Earth system model applied over a Pan-Arctic model domain. This dataset includes distributed daily coastal streamflows between 1979 and 2014 for the RASM domain. In Hamman et al. (2017) we demonstrate the performance of RASM and RVIC-simulated streamflow in fully coupled model simulations and discuss the improvements this derived dataset has, relative to existing distributed datasets in the Arctic.</p> <p>See the following references for further details on this dataset:</p> <p>Hamman, J., B. Nijssen, A. Roberts, A. Craig, W. Maslowski, and R. Osinski, 2017: The Coastal Streamflow Flux in the Regional Arctic System Model. Journal of Geophysical Research: Oceans, doi:10.1002/2016JC012323.</p> <p>Hamman, J., B. Nijssen, M. Brunke, J. Cassano, A. Craig, A. DuVivier, M. Hughes, D.P. Lettenmaier, W. Maslowski, R. Osinski, A. Roberts, and X. Zeng, 2016: Land surface climate in the Regional Arctic System Model. Journal of Climate, doi:10.1175/JCLI-D-15-0415.1.</p>
The Impact of a Pressurized Regional Sea or Global Ocean on Stresses on Enceladus: Numerical models
<p>Comsol Multiphysics models solving the stress field in Enceladus' ice shell when a regional sea of global ocean is pressurized.</p> <p>Model parameters are included as part of the file name according to the template EnceladusT<em>t</em>D<em>d</em><em>Label</em>.mph with</p> <ul> <li><em>t</em> is the ice shell thickness</li> <li><em>d</em> is the thickness of the south polar sea or indentation</li> <li><em>Label </em>indicates model configuration <ul> <li><em>Fixed</em>: The base of the ice shell (outside the south polar sea) is in contact with the core with a no-slip boundary condition</li> <li><em>Roller</em>: The base of the ice shell (outside the south polar sea) is in contact with the core with a free-slip boundary condition</li> <li><em>Ocean</em>: The base of the ice shell is floating with a constant pressure condition; there is a single indentation at the South pole</li> <li><em>North</em>: The base of the ice shell is floating with a constant pressure condition; there are indentations at both poles, with the north pole indentation having half the thickness of the South pole indentation</li> </ul> </li> </ul> <p>There are two solved datasets in each model. The first uses a default value of the ocean angle (40°). The second results from a parameter sweep in which the sea angle varies systematically in increments of 2°.</p>
MEHRA data and model for the English regions
<p><strong>M</strong>ulti-dimensional <strong>E</strong>nvironment-<strong>H</strong>ealth <strong>R</strong>isk <strong>A</strong>nalysis (<strong>MEHRA</strong>) data and model for the English regions</p> <p>This archive contains 4 objects in RDS (R Data Storage) format:</p> <ul> <li><strong>Training </strong>and <strong>testing</strong> datasets - These have been assembled to carry out the experiments described in the paper 'Modelling air pollution, climate and health data using Bayesian Networks: a case study of the English regions' by Vitolo et al. (currently under review)</li> <li><strong>BN model </strong>and <strong>DAG</strong> - These are the bayesian network and DAG resulting from the experiment described in the paper 'Modelling air pollution, climate and health data using Bayesian Networks: a case study of the English regions' by Vitolo et al. (currently under review)</li> </ul> <p>The paper contains full details of the features, below is a short summary:</p> <ul> <li>Data were collected in England (United Kingdom) from 1981 to 2014.</li> <li>Mortality counts were obtained from the Office for National Statistics (ONS)</li> <li>The counts were standardized based on yearly regional population estimates obtained from the MYEDE dataset.</li> <li>Data from air quality monitoring stations were obtained from the UK Air Information Resource service hosted by the Department for Environment, Food & Rural Affairs (DEFRA).</li> <li>Weather variables derive from ECMWF ERA-Interim (global re-analysis dataset)</li> </ul>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
Datasets related to the study "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach"
<p>This dataset contains the data of the manuscript "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach" under publication in Atmospheric Chemistry and Physics. <br>It includes CNRM-ALADIN64 simulations of surface solar radiation, cloud fraction, aerosol optical depth and water vapor content. <br>A directory is dedicated to HINDCAST simulations. It includes all datasets involved in the evaluation of CNRM-ALADIN64 simulations, as well as all datasets used for the analysis of the spatial variability of surface solar irradiance over the recent past. <br>Another directory is dedicated to future climate simulations. In this case, several sub directories can be found, representing either the simulations over the historical period (2005-2014, i.e. HIST directory), or simulations at mid (2045-2054, "mid" suffix) and long term (2091-2100, "end" suffix) horizons for SSP1-1.9 and SSP3-7.0. Each set of climate simulations is composed of three members (r1f, r2f, r3f), which were used collectively to increase the statistical significance of our analysis. </p>
Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »
<p>This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).</p> <p>It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.</p> <p>Five separate datasets are included:</p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Algeria<br>Angola<br>Benin<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Republic<br>Chad<br>Congo Republic<br>Democratic Republic of the Congo<br>Djibouti<br>Egypt<br>Equatorial Guinea<br>Eritrea<br>Eswatini<br>Ethiopia<br>Gabon<br>The Gambia<br>Ghana<br>Guinea<br>Guiné-Bissau<br>Côte d'Ivoire<br>Kenya<br>Lesotho<br>Liberia<br>Libya<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Morocco<br>Mozambique<br>Namibia<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>Somalia<br>South Africa<br>South Sudan<br>Sudan<br>Togo<br>Tunisia<br>Uganda<br>Tanzania<br>Zambia<br>Zimbabwe</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <span><a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></span></p> <p><strong>See also</strong></p> <p>Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a></p>
Data for "Random forest-based modeling of stream nutrients at national level in a data-scarce region"
<p>The aim of the study was to model annual total nitrogen (TN) and total phosphorus (TP) concentrations at national level using an ML approach. We used water quality data originating from the Environmental Monitoring Database KESE to train RF models for nutrient concentration prediction in 242 catchments across Estonia. A total of 82 environmental variables were used as predictors in the models. In order to yield the best results, a feature selection strategy along with hyperparameter optimization was performed when building the models. The models are applicable for predicting nutrient loads on an annual level, e.g. for the purpose of reporting national level water quality statistics in regional projects, such as HELCOM. The results showed that this relatively basic RF modeling approach can have a performance similar to process-based models. Moreover, these models are easier to reuse and apply on a larger scale, since the required inputs can be derived from freely available datasets (e.g. satellite imagery)</p> <p>This repository contains the input data used for building the RF models and the files describing the modeling results.</p> <p>The description of the files is given in the README.txt file.</p> <p>Virro, H., Kmoch, A., Vainu, M. and Uuemaa, E., 2022. Random forest-based modeling of stream nutrients at national level in a data-scarce region. Science of The Total Environment, 840, p.156613.</p> <p><a href="https://doi.org/10.1016/j.scitotenv.2022.156613">https://doi.org/10.1016/j.scitotenv.2022.156613</a></p>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis: Datasets
<p>This dataset includes output for snowfall, near-surface air temperature and melt from the following regional climate models (RCMs): Met Office Unified Model version 11.1 (MetUMv11.1), the Modèle Atmosphérique Régional version 3.10 (MARv3.10) and the Regional Atmospheric Climate Model version 2.3p2 (RACMOv2.3p2). The data is aggregated to monthly timesteps from initial 3/6hourly data. The code for aggregation is available here: https://github.com/Jez-Carter/Antarctica_Climate_Variability . Data goes from ~1971-2018 and includes two simulations from each RCM: 0.11° (12.25 km) and 0.44° (49 km) resolution simulations from the MetUM; ERA-Interim and ERA5 driven simulations from MAR and RACMO. The data used in the results for 'Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis Datasets' J.Carter et al, is included here and can be generated using the code available here: https://github.com/Jez-Carter/Antarctica_Climate_Variability . </p> <p><strong>Data usage notice:</strong><br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>"We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>In order to document MAR scientific impact and enable ongoing support of the model, users are encouraged to contact C. Kittel to add their works in the list of MAR-related publications.</p> <p>If you need other variables or output frequencies over Antarctica from: MAR, contact C.Kittel (c2kittel@gmail.com); RACMO, contact J.M. van Wessem; MetUM, contact A.Orr. </p>
RACMO regional climate model data, postprocessed for winter precipitation and winter temperature
<p>This contains statistics of winter precipitation and winter temperature derived from the 16 model ensemble by RACMO2. In addition to the GCM driven runs, also a PGW (pseudo global warming) set is given. Data is used for a paper to be submitted.</p> <p>Reference on the RACMO2 runs: Aalbers EE, Lenderink G, van Meijgaard E, van den Hurk BJJM (2018) Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50:4745–4766. <a href="https://doi.org/10.1007/s00382-017-3901-9">https://doi.org/10.1007/s00382-017-3901-9</a></p>
Lightning Assimilation in the Weather Research and Forecasting (WRF) Model: Technique Updates and Assessment of the Applications from Regional to Hemispheric Scales
<p>Figure 1. The data is proprietary, but it can be purchased from Vaisala Inc. (https:// <a href="http://www.vaisala.com/en/products/systems/lightning-detection">www.vaisala.com/en/products/systems/lightning-detection</a>), and the WWLLN raw data are also available for purchase at <a href="http://wwlln.net">http://wwlln.net</a>.</p> <p>Figure 2. Maps, data is not applicable.</p> <p>Figure 3. Data file: NLDN_WWLLN_Prism_Rainfall_Analysis.xlsx</p> <p>Figure 4. Data file: NLDN_WWLLN_METVARS_T2_Jul_2016.xlsx</p> <p>Figure 5. Data file: CONUSall_METOBS_q_Jul_2016.xlsx</p> <p>Figure 6. Data file: CONUSall_METOBS_ws_Jul_2016.xlsx</p> <p>Figure 7. Created using the R script: Hemi_Rain_ModelOnlyWGPM.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds, AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds, and GPM_WRF_Paired_rain2Hemispheric_July2016.rds.</p> <p>Figure 8. Created using the R script: Hemi_Rain_Aanlysis.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds and AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds.</p> <p>Figure 9. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 10. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 11. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 12. Created using the R script: CreateCPCdataforUSdomain_vs_Prism.R based on the R oject files: Prism_CFC_WRF*.rds</p> <p>Figure 13. Data file: Hemi_lta_METOBS_T2_Jul_2016.xlsx</p> <p>Figure 14. Data file: Hemi_lta_METOBS_q_Jul_2016.xlsx</p> <p> </p>
Input data of GridPath model for South America's MERCOSUR sub-region
<p><strong>This repository contains input data of the GridPath model for South America’s MERCOSUR sub-region, presented in the forthcoming paper entitled “Exploring sustainable electricity system development pathways in South America’s MERCOSUR sub-region” by the same authors. These data can be used in conjunction with the GridPath model (version v0.8.0), available at <a href="https://github.com/blue-marble/gridpath](https://github.com/blue-marble/gridpath)">https://github.com/blue-marble/gridpath</a>. Description of the key input data and the scenarios presented in the paper are provided in the <a href="https://docs.google.com/spreadsheets/d/1l7ImQtKHpYTPp9cMmTGHCdJB-wJvq7klwrwKmrtCrqw/edit?usp=sharing">Readme file</a>.</strong></p>
Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States
<p>We compared climatic relationships to insurance loss across the inland Pacific Northwest region of the United States, using a design matrix methodology, to identify optimum temporal windows for climate variables by county in relationship to wheat insurance loss due to drought. The results of our temporal window construction for water availability variables (precipitation, temperature, evapotranspiration, and the Palmer drought severity index [PDSI]) identified spatial patterns across the study area that aligned with regional climate patterns, particularly with regards to drought-prone counties of eastern Washington. Using these optimum time-lagged correlational relationships between insurance loss and individual climate variables, along with commodity pricing, we constructed a regression-based random forest model for insurance loss prediction and evaluation of climatic feature importance. Our cross-validated model results indicated that PDSI was the most important factor in predicting total seasonal wheat/drought insurance loss, with wheat pricing and potential evapotranspiration having noted contributions. Our overall regional model had a R<sup>2</sup> of 0.49 and a RMSE of $30.8 million. Model performance typically underestimated annual losses, with moderate spatial variability in terms of performance between counties.</p>
Datasets associated with "Quantifying debris thickness of debris-covered glaciers in the Everest region of Nepal through inversion of a sub-debris melt model"
<p>Datasets that accompany "Quantifying debris thickness of debris-covered glaciers in the Everest region of Nepal through inversion of a sub-debris melt model". These datasets include the debris thickness estimates derived including and excluding ponds (denoted as wponds and noponds, respectively), flux divergences, the shapefile of the 600 m boxes, the master 10 m DEM, the median x and y velocities, and the change in elevation for each pair of DEMs used in the study for Ngozumpa, Khumbu, and Imja-Lhotse Shar Glaciers. Debris thickness is in units of meters. Flux divergence is in units of meters per year. A negative flux divergence means the box is gaining mass and is called the emergence velocity; while positive flux divergence means the box is losing mass and is called the submergence velocity.</p>
Antarctic surface mass balance with the regional climate model MAR (1979–2015)
<p>Outputs of the regional climate model MAR v3.6.41 for Antarctica, resolution 35km + source code</p> <p>===========================================</p> <p>Cécile Agosta, 23 Jan 2019 </p> <p>cecile.agosta@gmail.com</p> <p>===========================================</p> <p>Grid specifications are given in MAR-ant35km-grid.nc (projection : EPSG 3031).</p> <p>* State variables are averages of daily means:</p> <p> TT > temperature (°C)</p> <p> ZZ > height above sea level (m)</p> <p> UU, VV > x-wind and y-wind in the stereographic grid (m s-1)</p> <p> UV > wind speed (m s-1)</p> <p>State variables ending with z (e.g. UUz) are interpolated on fixed altitude levels above the ground.</p> <p>State variables ending with p (e.g. UUp) are interpolated on fixed pressure levels.</p> <p>* SMB components are summed: kg m-2 month-1 for montly files, kg m-2 year-1 for annual files, kg m-2 year-1 for clim files</p> <p> snf > snowfall</p> <p> rnf > rainfall</p> <p> rof > run-off</p> <p> sbl > sublimation/condensation</p> <p> smb = snf + rnf - sbl - rof</p> <p> mlt > snowmelt</p> <p> rfz > refreezing</p> <p>If you use this data, please cite the final accepted version of this article:</p> <p>Agosta C., Amory C., Kittel C., Orsi A., Favier V., Gallée H., van den Broeke M.R., Lenaerts J.T., van Wessem J.M., & Fettweis X. (in review, 2018). Estimation of the Antarctic surface mass balance using MAR (1979-2015) and identification of dominant processes. <em>The Cryosphere Discussions</em>, 1–22, <a href="https://doi.org/10.5194/tc-2018-76">doi:10.5194/tc-2018-76</a>.</p> <p>Please contact me if you need other outputs (variables/daily or hourly time steps)</p>
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Allen Brain Atlas
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OpenNeuro
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